Capturing Detailed Structure from Patient-Doctor Conversations for Use in Clinical Documentation

ABSTRACT

A method and system is provided for assisting a user to assign a label to words or spans of text in a transcript of a conversation between a patient and a medical professional and form groupings of such labelled words or spans of text in the transcript. The transcript is displayed on an interface of a workstation. A tool is provided for highlighting spans of text in the transcript consisting of one or more words. Another tool is provided for assigning a label to the highlighted spans of text. This tool includes a feature enabling searching through a set of predefined labels available for assignment to the highlighted span of text. The predefined labels encode medical entities and attributes of the medical entities. The interface further includes a tool for creating groupings of related highlighted spans of texts. The tools can consist of mouse action or keystrokes or a combination thereof.

CROSS-REFERENCE TO RELATED DISCLOSURE

This application is a continuation of U.S. patent application Ser. No. 16/612,879, filed Nov. 12, 2019, which is a national stage application under 35 U.S.C. § 371 of International Application No. PCT/US2017/057640, filed Oct. 20, 2017, each of which are incorporated herein by reference in their entirety.

BACKGROUND

This disclosure is directed to a method and system for facilitating the annotation of transcribed audio or audio-visual recordings of medical encounters.

Conversations between patients and medical practitioners such as doctors and nurses and their conversations are often recorded. The record of the conversation, and a transcript, are part of the patient's medical record. The transcript can be created by a speech-to-text converter or created by a trained (human) medical transcriptionist listening to the recording.

A transcript without any annotation is of limited usefulness when it is reviewed by the physician, as they have to pore over many lines or pages of the transcript to find relevant information or understand the relatedness of different comments in the transcript.

Additionally, a collection of transcripts of medical encounters can be used to train machine learning models. Training a machine learning model requires a large amount of high quality training examples, i.e., labelled data. There is a need in the art for methods for facilitating the generation of transcripts of medical encounters that are annotated, that is, relevant words or phrases are highlighted and associated with medical concepts and grouped as being related to each other. This disclosure meets that need.

SUMMARY

In a first aspect, a method of facilitating annotation of a recording of a medical practitioner-patient conversation is disclosed. The method includes a step of generating a display of the transcribed audio recording (i.e., transcript), for example on the display of a workstation used by a human (“scribe labeler”) who is performing the annotation. A tool is provided for highlighting spans of text in the transcript consisting of one or more words. The tools can be simple mouse or keyboard shortcuts for selecting or highlighting one or more words.

The method further includes a step of providing a tool for assigning a label to the highlighted spans of text. The tool includes a feature for searching through a set of predefined labels available for assignment to the highlighted span of text. For example, when the scribe labeler highlights a word such as “stomachache” in the transcript a window pops up where the user can search through available labels, e.g. by scrolling or using a search tool. The labels encode medical entities (such as symptoms, medications, lab results, etc.) and attributes of the medical entities (e.g., severity, location, frequency, time of onset of a symptom entity).

In this document, the term “medical entities” is intended to refer to categories of discrete medical topics, such as symptoms, medications, lab results, vital signs, chief complaint, medical imaging, conditions, medical equipment, and so forth. The medical entities are predefined to be relevant to the context of the labelling task, and so in this case in one embodiment they could consist of the following list: medications, procedures, symptoms, vitals, conditions, social history, medical conditions, surgery, imaging, provider, vaccine, reproductive history, examination, and medical equipment. The medical entities could be structured in a hierarchical manner, such as the medical entity “medication” could be in the form of “medication:allergy” where “allergy” is a type or subclass of the overall class “medication.” As another example, the medical entity “symptom” could be structured in a hierarchical manner of symptoms for different parts of the body, such as “symptom:eyes”, “symptom:neurological”, etc.

The term “attributes of the medical entities” simply means some descriptive property or characteristic of the medical entity, such as for example the medical entity “medical equipment” may have an attribute of “patient's actual use” meaning that the patient is currently using a piece of medical equipment. As another example, a symptom medical entity may have an attribute of “onset.” A label of “symptom/onset” would be used as an annotation when there is word or phrase in the transcript indicating when the patient first started experiencing the symptom. As another example, a label of “medical equipment/regularly” would be used as an annotation when there is a word or phrase in the transcript indicating the patient used some piece of medical equipment regularly, with “regularly” being the attribute of the medical entity “medical equipment.”

The method further includes a step of providing a tool for grouping related highlighted spans of texts. The tool could be for example a combination of mouse clicks or keyboard shortcuts to establish the grouping. The groupings allow medical entities associated with labels assigned to the highlighted spans of text to be associated as a group. For example, in a conversation in which a patient describes a sharp chest pain that started last week, the text “sharp”, “chest pain” and “last week” would be highlighted and labeled with symptom labels and attributes of severity, location, and time of onset, respectively and grouped together as all being related to each other.

In another aspect, a system is disclosed for facilitating annotation of a recording of a medical practitioner-patient conversation. The system includes a) an interface displaying a transcript of the recording; b) a tool for highlighting spans of text in the transcript consisting of one or more words; c) a tool for assigning a label to the highlighted spans of text, wherein the tool includes a feature enabling searching through predetermined labels available for assignment to the highlighted span of text, and wherein the labels encode medical entities and attributes of the medical entities; and d) a tool for creating groupings of related highlighted spans of texts.

The methods and systems are applicable to other types of transcripts, in which a set of predefined labels are created, e.g., by an operator, which are designed to be relevant to the annotation task at hand and the labels are associated with entities and attributes relevant to the transcript and annotation task. The tools of this disclosure are used in the same manner in these other possible implementations, such as for example transcripts of legal proceedings, such as deposition or trial, or transcripts of hearings before administrative bodies, such a city council, Congress, State Legislature, etc.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart showing an environment in which the method can be performed.

FIG. 2 is an illustration of workstation having a display and user interface for use by a human (“scribe labeler”) to annotate a transcript of medical encounter. The user interface includes the tools described in conjunction with FIGS. 4-6 . The term “user interface” is intended to refer to the combination of the display on the workstation and associated devices for providing user input, such as the mouse and keyboard.

FIG. 3 is an illustration of the user interface of FIG. 2 showing a list of transcripts which are ready for annotation.

FIG. 4 is an illustration of a transcript of a medical encounter in which the scribe labeler is annotating certain words or phrases in the text. FIG. 4 shows a search box which pops up which permits the scribe labeler to search for medical entities and associated attributes. Spans of text can be highlighted by use of a tool such as by clicking on the word or using drag techniques with a mouse.

FIG. 5 is an illustration of the transcript of FIG. 4 in which the scribe labeler is annotating the text “upper left” and a search box which pops up. Additionally, a proposed label for the phrase “upper left” for the medical entity “symptom” and attribute “location (on body)” is also displayed. The proposed label is generated by a pre-labelling system shown in FIG. 1 .

FIG. 6 is illustration of the transcript of FIGS. 4 and 5 when the scribe labeler forms a grouping of the two highlighted spans of text “stomachache” and “three days”. The tool for forming the grouping consists of a highlighting the two texts and then keyboard shortcut of holding down the “G” key, clicking on the highlighted spans of text, and releasing the “G” key. FIG. 6 also shows the formation of the group in the Groups tab listing all the groups in the transcript at the bottom of the display.

FIG. 7 is a more detailed illustration of the pre-labeler of FIG. 1 .

FIG. 8 is an illustration of a machine learning model training system which receives as input a multitude of annotated transcripts in accordance with the features of FIG. 1 .

DETAILED DESCRIPTION

This disclosure is directed to methods and systems for facilitating annotations of recordings of medical encounters, i.e., conversations between patients and medical practitioners such as doctors or nurses. The recordings could be audio or audio-visual recordings. The recordings are transcribed into written form. The transcripts could be generated by trained medical transcriptionists, that is by hand, or by the use of speech to text converters, which are known in the art. The output of the system is an annotated version of the transcript in which relevant medical information (i.e., spans of text, such as individual words or groups of words) in the text are labeled (i.e., tagged as being associated with medical entities and attributes of such entities), and grouped to express relatedness between the labelled text.

FIG. 1 is a flow chart showing the environment in which the methods and systems of this disclosure are practiced. Patient consent for recording the encounter with the doctor or nurse is obtained at 102. Additionally, the patient is advised of the use of a transcript of the recording to be placed into the electronic health record and consent is obtained. The patient is further advised that the recording may be annotated and used for generating or training machine learning models and consent is obtained as well. In all cases where the transcripts are annotated or used for machine learning model training the transcript data is patient de-identified and used in compliance with all requirements for disclosure and use of a limited data set under HIPAA. Ethics review and institutional review board exemption is obtained from each institution. Patient data is not linked to any Google user data. Furthermore, for the system 116 using annotated transcripts for machine learning model training includes a sandboxing infrastructure that keeps each electronic health record (or transcript) dataset separated from each other, in accordance with regulation, data license and/or data use agreements. The data in each sandbox is encrypted; all data access is controlled on an individual level, logged, and audited.

At step 104, after the required patient consents are obtained, the patient consults with the medical practitioner and a recording, either audio or audio-visual, is obtained and stored in digital format.

At step 106, a written transcript of the recording is obtained, either by a trained transcriptionist or by use of a speech-to-text converter. The transcript is preferably accompanied by a time indexing, in which the words spoken in the transcript, or lines of text, are associated with elapsed time of the recording, as will be illustrated subsequently.

At step 108, an annotation of the transcript is performed by the scribe labeler in the manner described and explained in the subsequent figures. The annotations include the assignment of labels to spans of text in the transcript and groupings of spans of text to indicate their relatedness. In step 108 a display of the transcribed audio recording is generated, for example on the display of a workstation used by the scribe labeler. See FIGS. 2 and 4-6 . A tool is provided for highlighting spans of text in the transcribed audio recording consisting of one or more words. The tool can be simple mouse or keyboard shortcuts for selecting or highlighting one or more words. A tool is also provided for assigning a label to the highlighted spans of text. The tool includes a feature for searching through predetermined labels available for assignment to the highlighted span of text. For example, when the scribe labeler highlights a word such as “stomachache” in the transcript a list pops up where the user can search through available labels, and a search tool is provided for performing a word search through the list of labels. The labels encode medical entities (such as symptoms, medications, lab results, etc.) and attributes of the medical entities (e.g., severity, location, frequency, time of onset of a symptom entity).

A tool is also provided for grouping related highlighted spans of texts. The groupings allow medical entities associated with labels to be grouped together. For example, in a conversation in which a patient describes a sharp chest pain that started last week, the text “sharp”, “chest pain” and “last week” would be highlighted and labeled with symptom labels and attributes of severity, location, and time of onset, and grouped together, as they are all related to a single medical condition of the patient. This tool can consist of keyboard and/or or mouse action, as explained below.

The system may include a pre-labeler 110, shown in more detail in FIG. 7 . The pre-labeler is a computer system implementing a learned, automated word recognition model which identifies words or spans of text in the transcript which are likely to be the subject of labelling or grouping. The pre-labeler 110 provides input into annotation step 108 by providing suggested labels for highlighted spans of text when the scribe labeler performs the annotation of the transcript. This is shown in more detail in FIG. 5 .

As a result of the annotation step 108 an annotated transcript file 112 is created, which consists of the transcript as well as annotations in the form of labelled or tagged spans of text (words or phrases) and groupings of the tagged spans of text. The annotated transcript file is in digital form, with the annotations and groupings in the file as metadata or otherwise. The annotated transcript file 112 is then added to the patient's electronic health record (EHR) 114 or supplied to a machine learning model training system 116. The machine learning model training system 116 may, for example, be a system for training a machine learning model to automatically annotate transcripts of medical encounters. Alternatively, the machine learning model may use the annotated transcript as well as other data in the patient health record, for not only the individual patient, but also a multitude of other patients, to generate predictions of future medical events for example as described in the pending U.S. provisional application Ser. No. 62/538,112 filed Jul. 28, 2017, the content of which is incorporated by reference herein. The EHR 114 may be provided to the system 116 as indicated by the dashed line 114.

The annotated transcript file 112 may be fed back into the pre-labeler to enable further training the machine learning pre-labeler 110, as indicated by the dashed line 120. This aspect will be described in further detail later.

FIG. 2 is an illustration of a workstation 200 which is used by a scribe labeler during the annotation step 108 of FIG. 1 . The workstation includes a central processing unit (general purpose computer 210) executing an application which provides for display of the transcript of the medical encounter and tools by which the user interface consisting of a keyboard 212, a mouse 214 and a monitor 216 allow for the highlighting of spans of text (words or phrases 230), assigning labels to the spans of text, and grouping of the highlighted spans of text as will be discussed below. The monitor 216 includes a display 218 of a transcript 222, and a scroll bar 224 for allowing the user to navigate to various portions of the transcript. A time index 220 of the transcript is shown at the top of the display 218. The time index includes a slider 221 which when moved horizontally back and forth allows for the portion of the transcript associated with a particular elapsed time to be displayed at the top of the display 118. In this case the time index 220 indicates that the transcript is 13 minutes 24 seconds duration and the slider 221 is all the way to the left, therefore the beginning of the transcript is shown at the top of the display. The transcript is in the form of numbered lines, followed by identification of who was speaking (doctor or patient), followed by a text transcript of what was said.

FIG. 3 shows the display of a “to-do” list of transcripts in need of annotation which is provided on the user interface of FIG. 2 when the scribe labeler logs on to the workstation of FIG. 2 . The individual transcripts are patient de-identified (that is, identified only by patient number in column 302 and not by name). Column 304 shows the elapsed time, column 306 shows the number of lines of text in the transcript, column 308 shows the patient's chief complaint associated with the medical encounter, and column 310 shows the nature or type of the medical encounter. When one of the transcripts is selected in FIG. 3 (e.g., by clicking on the number in the column 302) the display of FIG. 2 is generated.

FIG. 4 is an illustration of the display 218 of the user interface along with a transcript 222, and time index 220. Time segment information for each utterance (sentence or word) is provided in the transcript and the time index 220 provides a slider tool 221 which moves right and left to jump to different portions of the transcript.

The interface provides a tool for text highlighting. In particular, mouse and keyboard shortcuts make highlighting spans of text easy. For example, a user can double click on a given word and the word is automatically highlighted on the display. Only words can be highlighted, not individual characters, reducing errors and increasing annotation speed. Other tools could be used for highlighting, such as by click and drag techniques with a mouse, a keyboard stroke (such as by putting the cursor over the word and hitting a particular key such as H, or CTRL-H), or a combination keyboard stroke and mouse action.

In the example of FIG. 4 , the user has highlighted the word “stomachache” (see 400). The user interface provides a tool for text tagging, i.e., labelling the highlighted term. Labels are applied to the highlighted spans of text essentially allowing the scribe labeler to inject information into the transcript, for example to indicate that the highlighted text “stomachache” is a symptom, or a gastrointestinal symptom. In particular, when the user has highlighted the term “stomachache”, a box (tool) 402 pops up which shows a list 404 of medical entities and associate attributes, a search term entry field 405 by which they can search the list 404, and a scroll bar 406 allowing the scribe labeler to scroll through the list and select a medical entity and associate attribute which is appropriate for the highlighted test. In the example FIG. 4 , the medical entity “Symptom:GI” and associated attribute “abdominal pain” was found in the list 404 and the user clicked on that combination of medical entity and attribute. The display includes a Table tab 410 at the bottom of the display which lists the labelled spans of text, including medical entity, attribute, location in the transcript (line 4) and the associated text span (“stomachache”).

The scribe labeler does the same process and uses the same tools to highlight the span of text “three days”, assign a label of medical entity “SymAttr” and attribute “duration” (“Symattr/duration”) to the highlighted span of text “three days” and this additional annotation shows up in the Table of annotations 410.

The scribe labeler then proceeds to highlight the span of text “upper left”, 412. The scribe labeler again uses the tool 402 to ascribe a label to the span of text “upper left.” Again this could be done using the tools described in FIG. 4 . As shown in FIG. 5 , in one embodiment where there is pre-labelling of the transcript, when the user highlights the span of text “upper left” a suggested label is shown in the box 502. This suggested label was assigned to the span of text “upper left” by the pre-labeler of FIG. 1 . The user can accept this suggestion by clicking on the box 502, or reject the suggestion by clicking on the X icon 504. In the situation of FIG. 5 the scribe labeler accepted the suggestion by a mouse click (or any other alternative suitable user interface action, such as keyboard shortcut etc.) and the annotation is added to the Table 410 as shown in FIG. 5 at 506. If the scribe labeler rejects the suggestion they can use the pop-up search tool 402 or scroll through the list of labels to find a suitable label.

It will be appreciated that the search tool 402 could pop up when the scribe labeler is taking action to highlight a span of text, and disappear after the label has been assigned, or alternatively it could be a persistent feature of the user interface during annotating.

As noted previously, the user interface of FIGS. 2 and 4-6 includes a tool for permitting the scribe labeler to group together highlighted and labelled spans of text which are conceptually or causally related to each other. For example, in FIG. 6 the spans of text “stomachache”, and “three days” are related to a gastrointestinal symptom, namely the type of symptom and the duration of the symptom. To make this grouping, the interface provides a tool in the form of combination of key strokes and mouse actions in the illustrated embodiment. In particular, the scribe labeler holds down the “G” key, clicks on the two highlighted spans of text, and then releases the “G” key. Of course, variations from this specific example of the tool for forming a grouping are possible and within the scope of this disclosure, such as combinations of mouse actions alone (e.g., selecting spans of text with a left click and then a right click to form the group), key strokes alone (e.g., ALT-G to select the highlighted spans of text and then ENTER to form the group), or other various possible combinations of mouse actions and key strokes. In FIG. 6 , the “2” icon 602 indicates the number of elements in the grouping (here two). The “X” icon 604 is click target to delete the grouping. The user has toggled the Groups tab 606 and the group of “stomachache” and “threedays” is shown as indicated at 608, along with the location in the transcript (line 4 for the location first element in the group in this example).

The search tool 402 of FIG. 4 makes the process of locating the relevant label easy to navigate. In the example of medical transcripts, there may many hundreds of possible labels to choose from. For example, there may be ten or twenty predefined different medical entities and ten or twenty or more different attributes for each of the medical entities. The medical entities may be customized and organized in a hierarchical manner, as explained previously. These labels encode a medical ontology that is designed specifically for medical documentation. These labels encode medical entity information, such as medication, procedures, symptoms, conditions, etc., as well as attributes of the entities, such as onset, severity, frequency, etc., of a symptom, and whether or not the patient declined or refused (attributes) a medical procedure (entity).

The text grouping as shown in FIG. 6 allows the scribe labeler to inject additional information into the transcript and in particular identify relationships or relatedness between concepts. For example the system and method of this disclosure allows the scribe labelers to specify groups of highlighted text such that entities can be associated with the attributes as a group.

The pre-labelling system 110 of FIG. 1 is shown in more detail in FIG. 7 . The input to the system 110 is a text transcript 702 generated at step 108 of FIG. 1 . The system 110 uses a machine learning medical named entity recognition (NER) model 703 which identifies candidate information (words or phrases) in the transcript and suggested labels for such words or phrases based on supervised learning from trained examples, in the form of a pre-annotated transcript 704. Named entity recognition models are well known in the field of machine learning and are described extensively in the scientific literature. The NER model 703 needs its owned labelled training data. For this training data we use a large corpus of medical text books (over 120,000 medical text books) using deep learning word embedding, in conjunction with a large lexicon of existing medical ontologies, e.g., UMLS (unified medical language system) and SNOMED (systemized nomenclature of medicine).

Additionally, the NER can be trained from annotated medical encounter transcripts. A NER model can also be trained from a hybrid of data sources, which may include medical and clinical text books, annotated transcripts from doctor-patient conversations, and clinical documentation contained in anonymized electronic health records of a multitude of patients. The NER model may further be trained from feedback of the annotation of the transcript as performed in FIG. 1 and FIG. 7 . For example, after the pre-labeling system generates the pre-annotated transcript 704 and the scribe labeler has proceeded to complete the annotation at step 108, there can be feedback of corrections between the suggested annotations in pre-annotated transcript 704 and annotated transcript 112 back into the NER model.

As shown in FIG. 8 , the annotated transcripts 112 can be supplied to a machine learning model training system. In one form, the model training system 116 uses the transcripts, along with other patient data, from a multitude of patients to generate machine learning models to make health predictions. Alternatively, the annotated transcripts could be used in the system 116 to develop deep learning models for automating the process of generating annotated transcripts of medical encounters.

The system and method of this disclosure has several advantages. In many natural language processing text annotation tools, relationships between must be identified in an explicit and cumbersome manner. In contrast, in this disclosure the labels (including predefined labels relevant to the annotation task) and labelling and groupings tools permit such relationships to be readily specified. The user can quickly search for labels by means of the search tools as shown in the Figures and select them with simple user interface action such as a click of a mouse. Moreover, groupings of conceptually or causally related highlighted spans of text can be created very quickly with simple user interface actions using a keyboard, mouse, or combination thereof, as explained above.

While the illustrated embodiment has described an interface and tools for assisting in labeling transcripts of medical encounters, the principles of this disclosure could be applied to other situations. In particular, a predefined list of labels is generated for entities and attributes of those entities, e.g., listing all the possible categories or classes of words of interest in a transcript and attributes associated with each of the categories or classes, analogous to the attributes of medical entities. The user interface actions described above would generally be performed in the same way, that is the scribe labeler would read the transcript and highlight words or other spans of text that are relevant to the annotation task, using simple user interface tools, and then tools would be enabled by which the scribe labeler could search through the available labels and assign them to the highlighted spans of text.

Additionally, grouping tools are provided to form groups of related highlighted spans of text. The result is an annotated transcript. The methods could have usefulness in other types of transcripts, such as deposition or trial transcripts in the context of the legal profession, hearing transcripts of testimony of governmental bodies, etc.

An example of a list of labels for use in annotation of medical transcripts is set forth below in Table 1. It will be understood of course that variation from the list is possible and that in other contexts other labels will be defined. In the list, Entity 1 is a medical entity and Entity 2 is either a subcategory of the medical entity of Entity 1 or an attribute of the medical entity, and Entity 3 is either an attribute of the medical entity or a further subcategory of the medical entity of Entity 1 in a hierarchical schema.

TABLE 1 Combined Label that will show Entity 1 Entity 2 Entity 3 up in labeling tool NOS NOS: SymAttr Time of Onset SymAttr: Time of Onset SymAttr Frequency/Tempo SymAttr: Frequency/Tempo SymAttr Duration SymAttr: Duration SymAttr Improving/Worsening SymAttr: Improving/Worsening SymAttr Location (on body) SymAttr: Location (on body) SymAttr Severity/Amount SymAttr: Severity/Amount SymAttr Characteristic/Quality SymAttr: Characteristic/Quality SymAttr Provoking Factor SymAttr: Provoking Factor SymAttr Alleviating Factor SymAttr: Alleviating Factor SymAttr Radiation SymAttr: Radiation SymAttr Not Experienced SymAttr: Not Experienced SymAttr SymAttr: Chief Chief Complaint:: Complaint Meds Meds:: Meds Physician's Meds: Physician's Intended Intended Status Status: Meds Physician's Active, Continued Meds: Physician's Intended Intended Status Status: Active, Continued Meds Physician's Active, Modified Meds: Physician's Intended Intended Status Status: Active, Modified Meds Physician's Recommended/To Meds: Physician's Intended Intended Status Start Status: Recommended/To Start Meds Physician's Completed/Finished/ Meds: Physician's Intended Intended Status Stopped Status: Completed/Finished/ Stopped Meds Patient's actual Meds: Patient's actual use: use Meds Patient's actual Yes, Regularly Meds: Patient's actual use: use Yes, Regularly Meds Patient's actual Yes, Intermittently Meds: Patient's actual use: use Yes, Intermittently Meds Patient's actual Yes, as Needed Meds: Patient's actual use: use Yes, as Needed Meds Patient's actual Stopped Meds: Patient's actual use: use Stopped Meds Patient's actual No Meds: Patient's actual use: use No Meds Side Effect Meds: Side Effect: Meds Side Effect Experienced Meds: Side Effect: Experienced Meds Side Effect No Meds: Side Effect: No Meds Benefit Meds: Benefit: Meds Benefit Experienced Meds: Benefit: Experienced Meds Benefit No Meds: Benefit: No Meds Dosage Meds: Dosage: Meds Quantity Meds: Quantity: Meds Frequency/Duration Meds: Frequency/Duration: Meds Instructions/Directions Meds: Instructions/Directions: Meds Route of Meds: Route of Administration: Administration Meds Indication Meds: Indication: Meds Allergy Meds: Allergy: Meds Allergy Yes Meds: Allergy: Yes Meds Allergy No Meds: Allergy: No Meds Allergy Reaction Meds: Allergy: Reaction Medical Medical Equipment:: Equipment Medical Physician's Medical Equipment: Equipment Intended Status Physician's Intended Status: Medical Physician's Active, Continued Medical Equipment: Equipment Intended Status Physician's Intended Status: Active, Continued Medical Physician's Active, Modified Medical Equipment: Equipment Intended Status Physician's Intended Status: Active, Modified Medical Physician's Recommended/To Medical Equipment: Equipment Intended Status Start Physician's Intended Status: Recommended/To Start Medical Physician's Completed/Finished/ Medical Equipment: Equipment Intended Status Stopped Physician's Intended Status: Completed/Finished/ Stopped Medical Patient's actual Medical Equipment: Patient's Equipment use actual use: Medical Patient's actual Yes, Regularly Medical Equipment: Patient's Equipment use actual use: Yes, Regularly Medical Patient's actual Yes, Intermittently Medical Equipment: Patient's Equipment use actual use: Yes, Intermittently Medical Patient's actual Yes, as Needed Medical Equipment: Patient's Equipment use actual use: Yes, as Needed Medical Patient's actual Stopped Medical Equipment: Patient's Equipment use actual use: Stopped Medical Patient's actual No Medical Equipment: Patient's Equipment use actual use: No Medical Side Effect Medical Equipment: Side Equipment Effect: Medical Side Effect Experienced Medical Equipment: Side Equipment Effect: Experienced Medical Side Effect No Medical Equipment: Side Equipment Effect: No Medical Benefit Medical Equipment: Benefit: Equipment Medical Benefit Experienced Medical Equipment: Benefit: Equipment Experienced Medical Benefit No Medical Equipment: Benefit: Equipment No Medical Dosage Medical Equipment: Dosage: Equipment Medical Quantity Medical Equipment: Quantity: Equipment Medical Frequency/Duration Medical Equipment: Equipment Frequency/Duration: Medical Instructions/Directions Medical Equipment: Equipment Instructions/Directions: Medical Indication Medical Equipment: Equipment Indication: Condition Condition:: Condition Status Condition: Status: Condition Status Active Condition: Status: Active Condition Status Recurrence Condition: Status: Recurrence Condition Status Inactive Condition: Status: Inactive Condition Status Remission Condition: Status: Remission Condition Status Resolved Condition: Status: Resolved Condition Time of Onset/ Condition: Time of Onset/ Duration Duration: Condition Physician certainty Condition: Physician certainty: Condition Physician certainty Provisional/ Condition: Physician certainty: Differential Provisional/Differential Condition Physician certainty Confirmed Condition: Physician certainty: Confirmed Condition Physician certainty Refuted Condition: Physician certainty: Refuted Condition Severity New Condition: Severity: New Condition Severity Stable Condition: Severity: Stable Condition Severity Improved Condition: Severity: Improved Condition Severity Worsening Condition: Severity: Worsening Condition Family Condition: Family: Condition Family History of (First Condition: Family: History of Degree) (First Degree) Condition Family History of (Non-first- Condition: Family: History of degree) (Non-first-degree) Surgery Surgery:: Surgery Status Surgery: Status: Surgery Status Completed Surgery: Status: Completed Surgery Status Planned/Anticipated Surgery: Status: Planned/ Anticipated Procedures/ Procedures/Other Tests:: Other Tests Procedures/ Status Procedures/Other Tests: Other Tests Status: Procedures/ Status Scheduled/Upcoming Procedures/Other Tests: Other Tests Status: Scheduled/Upcoming Procedures/ Status Completed Procedures/Other Tests: Other Tests Status: Completed Procedures/ Status Not done Procedures/Other Tests: Other Tests Status: Not done Procedures/ Status Declined/Refused Procedures/Other Tests: Other Tests Status: Declined/Refused Procedures/ Status Recommended Procedures/Other Tests: Other Tests Status: Recommended Procedures/ Result Procedures/Other Tests: Other Tests Result: Procedures/ Result Value/Result/Finding Procedures/Other Tests: Other Tests Result: Value/Result/Finding Procedures/ Result Normal Procedures/Other Tests: Other Tests Result: Normal Procedures/ Result Abnormal Procedures/Other Tests: Other Tests Result: Abnormal Labs Labs:: Labs Status Labs: Status: Labs Status Scheduled/Upcoming Labs: Status: Scheduled/ Upcoming Labs Status Completed Labs: Status: Completed Labs Status Declined/Refused Labs: Status: Declined/ Refused Labs Status Recommended Labs: Status: Recommended Labs Result Value/Result/Finding Labs: Result: Value/Result/ Finding Labs Result Normal Labs: Result: Normal Labs Result Abnormal Labs: Result: Abnormal Imaging Imaging:: Imaging Status Imaging: Status: Imaging Status Scheduled/Upcoming Imaging: Status: Scheduled/ Upcoming Imaging Status Completed Imaging: Status: Completed Imaging Status Declined/Refused Imaging: Status: Declined/ Refused Imaging Status Recommended Imaging: Status: Recommended Imaging Result Value/Result/Finding Imaging: Result: Value/Result/ Finding Imaging Result Normal Imaging: Result: Normal Imaging Result Abnormal Imaging: Result: Abnormal Vaccine Vaccine:: Vaccine Status Vaccine: Status: Vaccine Status Scheduled/Upcoming Vaccine: Status: Scheduled/ Upcoming Vaccine Status Completed Vaccine: Status: Completed Vaccine Status Declined/Refused Vaccine: Status: Declined/ Refused Vaccine Status Recommended Vaccine: Status: Recommended Provider Provider:: Provider Type Provider: Type: Provider Type Physician/Practitioner Provider: Type: Physician/ Practitioner Provider Type Other Health Provider: Type: Other Health Professional Professional Provider Status of Referral Provider: Status of Referral: Provider Status of Referral Recommended/To Provider: Status of Referral: Start Recommended/To Start Provider Status of Referral On-going Provider: Status of Referral On-going Provider Status of Referral Discontinued/Stopped Provider: Status of Referral: Discontinued/Stopped Provider Status of Referral Requested Provider: Status of Referral: Requested Provider Urgent/Emergency Provider: Urgent/Emergency Care Care: Provider Hospital Provider: Hospital: Provider Follow-up Visit Provider: Follow-up Visit: Patient Patient instructions/ instructions/ education/recommendation:: education/ recommendation Social Hx Social Hx:: Social Hx Lifestyle/Wellness Social Hx: Lifestyle/ Habits Wellness Habits: Social Hx Tobacco Social Hx: Tobacco: Social Hx Tobacco Active Social Hx: Tobacco: Active Social Hx Tobacco Second Hand Social Hx: Tobacco: Second Smoking Hand Smoking Social Hx Tobacco Former Social Hx: Tobacco: Former Social Hx Tobacco Never Social Hx: Tobacco: Never Social Hx Tobacco Current Social Hx: Tobacco: Current Quantity/Freq Quantity/Freq Social Hx Tobacco Former Quantity/Freq Social Hx: Tobacco: Former Quantity/Freq Social Hx Tobacco Counseling Social Hx: Tobacco: Counseling Social Hx Alcohol Social Hx: Alcohol: Social Hx Alcohol Active Social Hx: Alcohol: Active Social Hx Alcohol Denies Social Hx: Alcohol: Denies Social Hx Alcohol Former Social Hx: Alcohol: Former Social Hx Alcohol Never Social Hx: Alcohol: Never Social Hx Alcohol Current Social Hx: Alcohol: Current Quantity/Freq Quantity/Freq Social Hx Alcohol Former Quantity/Freq Social Hx: Alcohol: Former Quantity/Freq Social Hx Alcohol Counseling Social Hx: Alcohol: Counseling Social Hx Marijuana or Drug Social Hx: Marijuana or Drug Use Use: Social Hx Marijuana or Drug Active Social Hx: Marijuana or Drug Use Use: Active Social Hx Marijuana or Drug Former Social Hx: Marijuana or Drug Use Use: Former Social Hx Marijuana or Drug Never Social Hx: Marijuana or Drug Use Use: Never Social Hx Marijuana or Drug Current Social Hx: Marijuana or Drug Use Quantity/Freq Use: Current Quantity/Freq Social Hx Marijuana or Drug Former Quantity Social Hx: Marijuana or Drug Use Use: Former Quantity Social Hx Marijuana or Drug Counseling Social Hx: Marijuana or Drug Use Use: Counseling Social Hx Socio Economic Social Hx: Socio Economic Status Status: Social Hx Socio Economic Home Social Hx: Socio Economic Status Status: Home Social Hx Socio Economic Occupation Social Hx: Socio Economic Status Status: Occupation Social Hx Socio Economic Insurance Social Hx: Socio Economic Status Status: Insurance Social Hx Logistics Social Hx: Logistics: Social Hx Logistics Transportation Social Hx: Logistics: Transportation Social Hx Sexual History Social Hx: Sexual History: Social Hx Sexual History Active Social Hx: Sexual History: Active Social Hx Sexual History Inactive Social Hx: Sexual History: Inactive Social Hx Sexual History Never Social Hx: Sexual History: Never Social Hx Sexual History Quantity of Partners Social Hx: Sexual History: Quantity of Partners Social Hx Travel History Social Hx: Travel History: Code Status/ Code Status/End Code Status/End of Life: End of Life of Life Code Status/End of Life: Reproductive Reproductive Hx:: Hx Reproductive Gravida (Number Reproductive Hx: Gravida Hx of Pregnancies) (Number of Pregnancies): Reproductive Parity (Number of Reproductive Hx: Parity Hx Births Carried to a (Number of Births Carried to Viable Gestational Age) a Viable Gestational Age): Reproductive Number of Reproductive Hx: Number of Hx Premature Births Premature Births: Reproductive Number of Natural Reproductive Hx: Number of Hx Abortions/Miscarriages Natural Abortions/Miscarriages: Reproductive Number of Living Reproductive Hx: Number of Hx Children Living Children: Reproductive Currently Pregnant Reproductive Hx: Currently Hx Pregnant: Reproductive Current Reproductive Hx: Current Hx Gestational Age Gestational Age: Reproductive Anticipating Reproductive Hx: Anticipating Hx Planned or Unplanned Planned or Unplanned Pregnancy Pregnancy: Reproductive Infertility Issue Reproductive Hx: Infertility Hx Issue: Reproductive IVF Reproductive Hx: IVF: Hx Reproductive Last Menstrual Reproductive Hx: Last Hx Period Menstrual Period: Reproductive Menarche (Time Reproductive Hx: Menarche Hx of First Period) (Time of First Period): Vitals Ht Vitals: Ht: Vitals Ht Value/Result/Finding Vitals: Ht: Value/Result/Finding Vitals Ht Normal Vitals: Ht: Normal Vitals Ht Abnormal Vitals: Ht: Abnormal Vitals Wt Vitals: Wt: Vitals Wt Value/Result/Finding Vitals: Wt: Value/Result/Finding Vitals Wt Normal Vitals: Wt: Normal Vitals Wt Abnormal Vitals: Wt: Abnormal Vitals BMI Vitals: BMI: Vitals BMI Value/Result/Finding Vitals: BMI :Value/Result/Finding Vitals BMI Normal Vitals: BMI: Normal Vitals BMI Abnormal Vitals: BMI: Abnormal Vitals Temp Vitals: Temp: Vitals Temp Value/Result/Finding Vitals: Temp: Value/Result/Finding Vitals Temp Normal Vitals: Temp: Normal Vitals Temp Abnormal Vitals: Temp: Abnormal Vitals HR Vitals: HR: Vitals HR Value/Result/Finding Vitals: HR: Value/Result/Finding Vitals HR Normal Vitals: HR: Normal Vitals HR Abnormal Vitals: HR: Abnormal Vitals BP Vitals: BP: Vitals BP Value/Result/Finding Vitals: BP: Value/Result/Finding Vitals BP Normal Vitals: BP: Normal Vitals BP Abnormal Vitals: BP: Abnormal Vitals Resp Rate Vitals: Resp Rate: Vitals Resp Rate Value/Result/Finding Vitals: Resp Rate: Value/Result/Finding Vitals Resp Rate Normal Vitals: Resp Rate: Normal Vitals Resp Rate Abnormal Vitals: Resp Rate: Abnormal Vitals O2 Vitals: O2: Vitals O2 Value/Result/Finding Vitals: O2: Value/Result/Finding Vitals O2 Normal Vitals: O2: Normal Vitals O2 Abnormal Vitals: O2: Abnormal Exam General Exam: General: Exam General Value/Result/Finding Exam: General: Value/Result/Finding Exam Const Exam: Const: Exam Const Value/Result/Finding Exam: Const: Value/Result/Finding Exam Eyes Exam: Eyes: Exam Eyes Value/Result/Finding Exam: Eyes: Value/Result/Finding Exam ENMT Exam: ENMT: Exam ENMT Value/Result/Finding Exam: ENMT : Value/Result/Finding Exam Dental Exam: Dental: Exam Dental Value/Result/Finding Exam: Dental: Value/Result/Finding Exam Neck Exam: Neck: Exam Neck Value/Result/Finding Exam: Neck: Value/Result/Finding Exam Resp/Pulm Exam: Resp/Pulm: Exam Resp/Pulm Value/Result/Finding Exam: Resp/Pulm: Value/Result/ Finding Exam CV Exam: CV: Exam CV Value/Result/Finding Exam: CV: Value/Result/Finding Exam Lymph Exam: Lymph: Exam Lymph Value/Result/Finding Exam: Lymph: Value/Result/Finding Exam GU Exam: GU: Exam GU Value/Result/Finding Exam: GU: Value/Result/Finding Exam MSK Exam: MSK: Exam MSK Value/Result/Finding Exam: MSK: Value/Result/Finding Exam Derm Exam: Derm: Exam Derm Value/Result/Finding Exam: Derm: Value/Result/Finding Exam Neuro Exam: Neuro: Exam Neuro Value/Result/Finding Exam: Neuro: Value/Result/Finding Exam Abd Exam: Abd: Exam Abd Value/Result/Finding Exam: Abd: Value/Result/Finding Exam Breast Exam: Breast: Exam Breast Value/Result/Finding Exam: Breast: Value/Result/Finding Exam Rectal Exam: Rectal: Exam Rectal Value/Result/Finding Exam: Rectal: Value/Result/Finding Exam Prostate Exam: Prostate: Exam Prostate Value/Result/Finding Exam: Prostate: Value/Result/Finding Exam Hernia Exam: Hernia: Exam Hernia Value/Result/Finding Exam: Hernia: Value/Result/Finding Exam Bimanual/GYN Exam: Bimanual/GYN: Exam Bimanual/GYN Value/Result/Finding Exam: Bimanual/GYN: Value/Result/Finding Exam Psych Exam: Psych: Exam Psych Value/Result/Finding Exam: Psych: Value/Result/Finding Exam Extremities Exam: Extremities: Exam Extremities Value/Result/Finding Exam: Extremities: Value/Result/ Finding Sym Const Sym: Const:: Sym Const Fever Sym: Const: Fever: Sym Const Chills Sym: Const: Chills: Sym Const Night Sweats Sym: Const: Night Sweats: Sym Const Body Aches Sym: Const: Body Aches: Sym Const Pain (Non-specific) Sym: Const: Pain (Non-specific): Sym Const Fatigue Sym: Const: Fatigue: Sym Const Lightheadedness Sym: Const: Lightheadedness: Sym Const Difficulty Sleeping Sym: Const: Difficulty Sleeping: Sym Const General Weakness Sym: Const: General Weakness: Sym Const Weight Loss Sym: Const: Weight Loss: Sym Const Weight Gain Sym: Const: Weight Gain: Sym Eyes Sym: Eyes:: Sym Eyes Change in Vision Sym: Eyes: Change in Vision: Sym Eyes Double Vision Sym: Eyes: Double Vision (Diplopia) (Diplopia): Sym Eyes Flashers (Photopsia) Sym: Eyes: Flashers (Photopsia): Sym Eyes Sensitivity to Light Sym: Eyes: Sensitivity to Light (Photophobia) (Photophobia): Sym Eyes Eye Pain Sym: Eyes: Eye Pain: Sym Eyes Eye Discharge Sym: Eyes: Eye Discharge: Sym Eyes Red Eye Sym: Eyes: Red Eye: Sym Eyes Dry Eye Sym: Eyes: Dry Eye: Sym ENMT Sym: ENMT:: Sym ENMT Ear Pain (Otalgia) Sym: ENMT: Ear Pain (Otalgia): Sym ENMT Ear Discharge Sym: ENMT: Ear Discharge (Otorrhea) (Otorrhea): Sym ENMT Swollen Ear Sym: ENMT: Swollen Ear: Sym ENMT Hearing Loss Sym: ENMT: Hearing Loss: Sym ENMT Sensitivity to Sounds Sym: ENMT: Sensitivity to (Phonophobia or Sounds (Phonophobia or Hyperacusis) Hyperacusis): Sym ENMT Ear Ringing Sym: ENMT: Ear Ringing (Tinnitus) (Tinnitus): Sym ENMT Nose Bleeding Sym: ENMT: Nose Bleeding (Epistaxis) (Epistaxis): Sym ENMT Nasal Discharge Sym: ENMT: Nasal Discharge (Rhinorrhea) (Rhinorrhea): Sym ENMT Nasal Congestion Sym: ENMT: Nasal Congestion: Sym ENMT Loss of Smell Sym: ENMT: Loss of Smell: Sym ENMT Sinus Issue Sym: ENMT: Sinus Issue: Sym ENMT Sore Throat Sym: ENMT: Sore Throat: Sym ENMT Oral Sores/Lesions Sym: ENMT: Oral Sores/Lesions: Sym ENMT Painful Swallowing Sym: ENMT: Painful Swallowing (Odynophagia) (Odynophagia): Sym ENMT Loss of Taste Sym: ENMT: Loss of Taste: Sym ENMT Tooth Pain Sym: ENMT: Tooth Pain: Sym ENMT Bleeding Gums Sym: ENMT: Bleeding Gums (Gingival Hemorrhage) (Gingival Hemorrhage): Sym ENMT Hoarse Voice Sym: ENMT: Hoarse Voice: Sym ENMT Change in Voice Sym: ENMT: Change in Voice: Sym ENMT Neck Pain Sym: ENMT: Neck Pain: Sym ENMT Change of Taste Sym: ENMT: Change of Taste (Dysgeusia) (Dysgeusia): Sym CV Sym: CV:: Sym CV Chest Pain (Angina) Sym: CV: Chest Pain (Angina): Sym CV Palpitations Sym: CV: Palpitations: Sym CV Leg Swelling (Edema) Sym: CV: Leg Swelling (Edema): Sym CV Leg Pain with walking Sym: CV: Leg Pain with walking (Claudication) (Claudication): Sym CV Fainting/Syncope Sym: CV: Fainting/Syncope: Sym Resp Sym: Resp:: Sym Resp Cough Sym: Resp: Cough: Sym Resp Hemoptysis Sym: Resp: Hemoptysis: Sym Resp Wheezing Sym: Resp: Wheezing: Sym Resp SOB Lying Flat Sym: Resp: SOB Lying Flat (Orthopnea) (Orthopnea): Sym Resp SOB when Waking Sym: Resp: SOB when Waking up (Paroxysmal up (Paroxysmal Nocturnal Nocturnal Dyspnea) Dyspnea): Sym Resp SOB Sym: Resp: SOB: Sym GI Sym: GI:: Sym GI Decreased Appetite Sym: GI: Decreased Appetite: Sym GI Nausea Sym: GI: Nausea: Sym GI Vomiting Sym: GI: Vomiting: Sym GI Difficulty Swallowing Sym: GI: Difficulty Swallowing (Dysphagia) (Dysphagia): Sym GI Jaundice Sym: GI: Jaundice: Sym GI Abdominal Pain Sym: GI: Abdominal Pain: Sym GI Abdominal Distension Sym: GI: Abdominal Distension: Sym GI Change in Bowel Sym: GI: Change in Bowel Habits Habits: Sym GI Diarrhea Sym: GI: Diarrhea: Sym GI Constipation Sym: GI: Constipation: Sym GI Stool Incontinence Sym: GI: Stool Incontinence (Encopresis) (Encopresis): Sym GI Bright Red Blood Per Sym: GI: Bright Red Blood Per Rectum (Hematochezia) Rectum (Hematochezia): Sym GI Black Tarry Stools Sym: GI: Black Tarry Stools (Melena) (Melena): Sym GI GI Bleeding (Non- Sym: GI: GI Bleeding (Non- specific) specific): Sym GI Anal Pain Sym: GI: Anal Pain: Sym GU Sym: GU:: Sym GU Pelvic Pain Sym: GU: Pelvic Pain: Sym GU Sexually Transmitted Sym: GU: Sexually Transmitted Disease Exposure Disease Exposure: Sym GU Frequent Urination Sym: GU: Frequent Urination: Sym GU Decreased Urination Sym: GU: Decreased Urination: Sym GU Urgent Urination Sym: GU: Urgent Urination: Sym GU Urinary Hesitancy Sym: GU: Urinary Hesitancy: Sym GU Burning with Sym: GU: Burning with Urination (Dysuria) Urination (Dysuria): Sym GU Blood in urine Sym: GU: Blood in urine (Hematuria) (Hematuria): Sym GU Changes in Urine Sym: GU: Changes in Urine Quality (Non-bloody) Quality (Non-bloody): Sym GU Incomplete Bladder Sym: GU: Incomplete Bladder Emptying (Urinary Emptying (Urinary Retention): Retention) Sym GU Urinary Incontinence Sym: GU: Urinary Incontinence: Sym GU Penile Discharge Sym: GU: Penile Discharge: Sym GU Penile Ulcers Sym: GU: Penile Ulcers: Sym GU Testicular Pain Sym: GU: Testicular Pain: Sym GU Scrotal Sym: GU: Scrotal Mass/Swelling Mass/Swelling: Sym GU Difficulty Obt. or Sym: GU: Difficulty Obt. or Maint. an Erection Maint. an Erection (Erectile (Erectile Dysfunction) Dysfunction): Sym GU Heavy Menstrual Sym: GU: Heavy Menstrual Bleeding (Menorrhagia) Bleeding (Menorrhagia): Sym GU Menstrual Regularity Sym: GU: Menstrual Regularity: Sym GU Irregular Menstrual Sym: GU: Irregular Menstrual Bleeding (Metrorrhagia) Bleeding (Metrorrhagia): Sym GU Vaginal Bleeding Sym: GU: Vaginal Bleeding after Menopause after Menopause: Sym GU Menstrual Pain Sym: GU: Menstrual Pain (dysmenorrhea) (dysmenorrhea): Sym GU Vaginal Discharge Sym: GU: Vaginal Discharge: Sym GU Vaginal/Vulva ulcers Sym: GU: Vaginal/Vulva ulcers: Sym GU Pain with Sexual Sym: GU: Pain with Sexual Intercourse (Dyspareunia) Intercourse (Dyspareunia): Sym GU Vaginal Dryness Sym: GU: Vaginal Dryness: Sym MSK Sym: MSK:: Sym MSK Pain Sym: MSK: Pain: Sym MSK Swelling Sym: MSK: Swelling: Sym MSK Decreased Range Of Sym: MSK: Decreased Range Of Motion Motion: Sym Skin/Br Sym: Skin/Br:: Sym Skin/Br Rash Sym: Skin/Br: Rash: Sym Skin/Br Stria Sym: Skin/Br: Stria: Sym Skin/Br Wounds Sym: Skin/Br: Wounds: Sym Skin/Br Incisions Sym: Skin/Br: Incisions: Sym Skin/Br Scrapes Sym: Skin/Br: Scrapes: Sym Skin/Br Sores/Ulcers Sym: Skin/Br: Sores/Ulcers: Sym Skin/Br Skin Darkening Sym: Skin/Br: Skin Darkening: Sym Skin/Br Hair Loss Sym: Skin/Br: Hair Loss: Sym Skin/Br Thinning Hair Sym: Skin/Br: Thinning Hair: Sym Skin/Br Sun sensitivity Sym: Skin/Br: Sun sensitivity: Sym Skin/Br Skin Itch (Pruritis) Sym: Skin/Br: Skin Itch (Pruritis): Sym Skin/Br Breast Lumps Sym: Skin/Br: Breast Lumps: Sym Skin/Br Breast Pain Sym: Skin/Br: Breast Pain: Sym Skin/Br Nipple Discharge Sym: Skin/Br: Nipple Discharge: Sym Skin/Br Fingers/Toes/Extremities Sym: Skin/Br: Fingers/Toes/ Turn Colors in Cold Extremities Turn Colors in Cold: Sym Skin/Br Nail Issue Sym: Skin/Br: Nail Issue: Sym Skin/Br Dry Skin Sym: Skin/Br: Dry Skin: Sym Skin/Br Scalp Tenderness Sym: Skin/Br: Scalp Tenderness: Sym Neuro Sym: Neuro:: Sym Neuro Pain Sym: Neuro: Pain: Sym Neuro Sensation changes Sym: Neuro: Sensation changes (Numbness/Coldness/ (Numbness/Coldness/Crawling/ Crawling/Prickling/ Prickling/Parasthesias): Parasthesias) Sym Neuro Memory Loss Sym: Neuro: Memory Loss: Sym Neuro Difficulty Thinking/ Sym: Neuro: Difficulty Thinking/ Changes in Mentation Changes in Mentation: Sym Neuro Seizures Sym: Neuro: Seizures: Sym Neuro Tremor Sym: Neuro: Tremor: Sym Neuro Dizziness Sym: Neuro: Dizziness: Sym Neuro Speech Problems Sym: Neuro: Speech Problems: Sym Neuro Non-general Sym: Neuro: Non-general Weakness Weakness: Sym Neuro Muscle Cramps Sym: Neuro: Muscle Cramps: Sym Neuro Jaw Pain with Sym: Neuro: Jaw Pain with Chewing Chewing: Sym Neuro Headache Sym: Neuro: Headache: Sym Psych Sym: Psych:: Sym Psych Anxiety Sym: Psych: Anxiety: Sym Psych Depressed Mood Sym: Psych: Depressed Mood: Sym Psych Feeling of Failure Sym: Psych: Feeling of Failure: Sym Psych Psychomotor Sym: Psych: Psychomotor Agitation or Agitation or Retardation: Retardation Sym Psych Sadness Sym: Psych: Sadness: Sym Psych Anhedonia Sym: Psych: Anhedonia: Sym Psych Manic Episodes Sym: Psych: Manic Episodes: Sym Psych Change in Sym: Psych: Change in Personality Personality: Sym Psych Paranoia Sym: Psych: Paranoia: Sym Psych Hallucinations Sym: Psych: Hallucinations: Sym Psych Irritability/Mood Sym: Psych: Irritability/Mood Swings Swings: Sym Psych Wake up Sym: Psych: Wake up Unrefreshed Unrefreshed: Sym Psych Stress Sym: Psych: Stress: Sym Psych Suicidality Sym: Psych: Suicidality: Sym Psych Homicidality Sym: Psych: Homicidality: Sym Psych Changes in Sexual Sym: Psych: Changes in Sexual Arousal Arousal: Sym Endo Sym: Endo:: Sym Endo Heat Intolerance Sym: Endo: Heat Intolerance: Sym Endo Cold Intolerance Sym: Endo: Cold Intolerance: Sym Endo Excessive Thirst Sym: Endo: Excessive Thirst (Polydipsia) (Polydipsia): Sym Endo Excessive Appetite Sym: Endo: Excessive Appetite (Polyphagia) (Polyphagia): Sym Endo Excessive Sweating Sym: Endo: Excessive Sweating: Sym Endo Flushing Sym: Endo: Flushing: Sym Endo Hot Flashes Sym: Endo: Hot Flashes (Vasomotor (Vasomotor symptoms): symptoms) Sym Heme/Lymph Sym: Heme/Lymph:: Sym Heme/Lymph Lymph Node Sym: Heme/Lymph: Lymph Node Enlargement/Tenderness Enlargement/Tenderness: Sym Heme/Lymph Easy Bruising Sym: Heme/Lymph: Easy Bruising: Sym Heme/Lymph Easy Bleeding Sym: Heme/Lymph: Easy Bleeding: Sym Immuno Sym: Immuno:: Sym Immuno Anaphylaxis Sym: Immuno: Anaphylaxis: Sym Immuno Hives (Urticaria) Sym: Immuno: Hives (Urticaria): Sym Immuno Frequent Sneezing Sym: Immuno: Frequent Sneezing: Sym Immuno Seasonal Allergies Sym: Immuno: Seasonal Allergies: Sym Immuno Environmental Sym: Immuno: Environmental Allergies Allergies: Sym Immuno Exposure to Sym: Immuno: Exposure to infectious diseases infectious diseases (TB, HIV, (TB, HIV, etc.) etc.): Sym Sym::: Sym Suggest Entity Sym: Suggest Entity:: 

What is claimed is:
 1. A computer-implemented method comprising: receiving a training dataset comprising a plurality of labeled transcripts of respective medical practitioner-patient conversations, each labeled transcript having been generated by a user interaction with the transcript, the user interaction having comprised of a user relating two different highlighted spans of text by utilizing an input device of a computing device, and the computing device having been configured to interpret the user interaction to be an indication that the two user-related different highlighted spans of text are medically related to a same health condition of the patient; training, based on the training dataset, a machine learning model to automatically annotate an input transcript of a recording of a medical conversation; and outputting the trained machine learning model.
 2. The method of claim 1, wherein the transcript of the recording is indexed to time segment information.
 3. The method of claim 1, wherein the automatic annotation of the input transcript comprises storing the annotation of the input transcript as metadata associated with the input transcript.
 4. The method of claim 1, wherein each highlighted span of text in the transcript is associated with a label encoding a medical entity and one or more attributes of the medical entity appearing in the highlighted span, wherein medical entities indicate categories of medical topics, and wherein medical attributes indicate descriptive properties or characteristics of an associated medical entity.
 5. The method of claim 4, wherein the medical entity comprises one or more of medications, procedures, symptoms, vitals, lab results, chief complaint, social history, medical conditions, surgery, imaging, provider, vaccine, reproductive history, examination, and medical equipment.
 6. The method of claim 5, wherein the medical entity is a symptom medical entity comprising an attribute of at least severity, frequency, onset, or location of a medical condition.
 7. The method of claim 1, further comprising: generating the training dataset by providing a plurality of unlabeled transcripts to a pre-labeling system and receiving, from the pre-labeling system a pre-annotated transcript containing suggested labels for spans of text in each of the plurality of unlabeled transcripts.
 8. The method of claim 7, wherein the pre-labeling system includes a named entity recognition model trained on at least one of medical textbooks, a lexicon of clinical terms, clinical documentation in electronic health records, and annotated transcripts of doctor-patient conversations.
 9. The method of claim 1, further comprising: providing the annotated version of the input transcript to another computing device for another user interaction with the annotated version, wherein the other user interaction comprises modifications including one or more additional annotations of the annotated version; receiving, from the other computing device, the modified version of the annotated transcript; and fine-tuning the trained machine learning model based on the modified version.
 10. A computer-implemented method comprising: receiving an input transcript of a recording of a medical practitioner-patient conversation; applying a trained machine learning model to automatically annotate the input transcript, the machine learning model having been trained on a training dataset comprising a plurality of labeled transcripts of respective medical conversations, each labeled transcript having been generated by a user interaction with the transcript, the user interaction having comprised of a user relating two different highlighted spans of text by utilizing an input device of a computing device, and the computing device having been configured to interpret the user interaction to be an indication that the two user-related different highlighted spans of text are medically related to a same health condition of the patient; and providing the annotated version of the input transcript.
 11. The method of claim 10, further comprising: generating the input transcript of the recording.
 12. The method of claim 10, further comprising: generating a display of the input transcript of the recording.
 13. The method of claim 10, wherein the input transcript is indexed to time segment information.
 14. The method of claim 10, wherein the automatic annotation of the input transcript comprises storing the annotation of the input transcript as metadata associated with the input transcript.
 15. The method of claim 10, wherein the providing of the annotated version comprises associating each highlighted span of text in the input transcript with a label encoding a medical entity and one or more attributes of the medical entity appearing in the highlighted span, wherein medical entities indicate categories of medical topics, and wherein medical attributes indicate descriptive properties or characteristics of an associated medical entity.
 16. The method of claim 15, wherein the medical entity comprises one or more of medications, procedures, symptoms, vitals, lab results, chief complaint, social history, medical conditions, surgery, imaging, provider, vaccine, reproductive history, examination, and medical equipment.
 17. The method of claim 15, wherein the medical entity is a symptom medical entity comprising an attribute of at least severity, frequency, onset, or location of a medical condition.
 18. A system comprising: a computing device; and data storage, wherein the data storage has stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computing device to carry out functions comprising: receiving an input transcript of a recording of a medical practitioner-patient conversation; applying a trained machine learning model to automatically annotate the input transcript, the machine learning model having been trained on a training dataset comprising a plurality of labeled transcripts of respective medical conversations, each labeled transcript having been generated by a user interaction with the transcript, the user interaction having comprised of a user relating two different highlighted spans of text by utilizing an input device of a computing device, and the computing device having been configured to interpret the user interaction to be an indication that the two user-related different highlighted spans of text are medically related to a same health condition of the patient; and providing the annotated version of the input transcript.
 19. The system of claim 18, further comprising: generating the input transcript of the recording.
 20. The system of claim 18, wherein the providing of the annotated version comprises associating each highlighted span of text in the input transcript with a label encoding a medical entity and one or more attributes of the medical entity appearing in the highlighted span, wherein medical entities indicate categories of medical topics, and wherein medical attributes indicate descriptive properties or characteristics of an associated medical entity. 